Neural Coding of Dynamic Stimuli

Authors:
Stefan D. Wilke
Affiliations:
-
Venue:
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Year:
2001

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Abstract

An estimation-theoretical approach is used to characterize the accuracy by which a pair of neurons encodes a time-varying stimulus. Whether high firing rates yield a representational advantage depends on the type of noise involved in spike generation: High rates are favorable for Poissonian noise, but fail to yield improvements in the case of multiplicative Gaussian noise. Moreover, we find that the performance of single-phase stimulus filters increases monotonically with decreasing temporal width, while biphasic filters have a unique optimal time scale that roughly corresponds to the stimulus autocorrelation time. This study demonstrates that estimation-theoretic methods, which have previously been used mainly for static stimuli, can also yield quantitative results on neural codes for dynamic stimuli.